How We Built Personalized Landing Pages with OpenAI/ChatGPT and Snitcher in Less Than an Hour

While I myself am personally both terrified and intrigued by AI all at once (more terrified about the long-term impacts, if I'm honest), one thing that is indisputable is that AI allows us to test ideas and concepts quickly and easily. A major frustration for me is the performance of landing pages. We spend a decent amount of money on PPC (or SEM or CPC or whatever you prefer to call it) via Google Ads. Primarily, we advertise for our Accessibility testing services, which tends to be a quicker sale than design and development work, and generally performs well with PPC . As any business owner knows, watching traffic come to your site only to bounce away quickly is infuriating. Just fill out the form already!

But what if we can just change content on the fly to make it that much more personalized?

Turns out that it's pretty easy to do – if you know what you are doing. That's why we set out to create a quick, effective experiment for landing page personalization that combines the power of Snitcher and OpenAI.

In less than an hour, we built a system that dynamically generates personalized headlines and subheadlines for website visitors in real-time. Using Snitcher, we can identify visitors by their company and industry, and with the OpenAI API, we generate tailored, professional content within strict guardrails. The result? A landing page that feels like it was custom-built for each visitor—without requiring days of development or a team of copywriters.

Best of all, we wrote 99% of the code using ChatGPT.

In this post, we'll walk you through how we achieved this, from integrating Snitcher to engineering prompts for consistent results with OpenAI. Whether you're an agency looking to enhance client experiences or a business wanting to maximize engagement, this guide will show you how to deliver effective personalization with minimal effort. It should also show you how AI tools have made experimentation like this quick, easy and low-risk.

For reference, here is the area of specific copy we are adjusting with our scripts. I'll let you find it yourself rather than make it too easy to mess up my conversion rate figures ;)

Step 1: Using Snitcher for Live Visitor Identification

The first step in building our personalized landing page was leveraging Snitcher to identify website visitors in real-time. Snitcher makes it easy to gather company and industry information about visitors based on their IP addresses. This allows us to tailor content to their specific context. While I'm not a fan of Snitcher's pricing – which has gone through the roof – the data is helpful.

We had already previously integrated Snitcher's JavaScript tracking code into our landing page, enabling it to identify visitors. Now, we could also trigger a callback function whenever visitor data is available. This callback provided us with key information such as the company name and industry of the visitor directly on the page to manipulate and utilize.

For example, if a visitor from "Harvard University" in the "Education" industry landed on our page, Snitcher would pass that information to our script. Of course, it has much more information than that, but to start, that was enough data to help generate some customized content.

To use ChatGPT most effectively, we simply gave it the URL to the API documentation to understand how it worked. In fairness, at first it did make some mistakes. It attempted to connect to an API endpoint that didn't exist – and it didn't need to – the access to the API was already given in the initial Snitcher embed code. It also made a few other minor mistakes, but generally it did very well.

Step 2: Sending Data to OpenAI for Dynamic Headline Generation

With visitor data in hand, the next step was to create a lightweight backend API using PHP. This API served as the middleman between our landing page and OpenAI's API. Its job was simple:

1. Accept the company name and industry as input.
2. Construct a prompt for OpenAI to generate personalized headlines and subheadlines.
3. Return the generated content in JSON format for easy integration.

I'll dig more into the specific prompt we used on the next step.

The PHP API also logged each request, including the timestamp, visitor data, and generated content, for debugging and analysis. Using this log we'll further train the AI to make better judgments about headline generation.

Step 3: Prompt Engineering to Maintain Guardrails

One of the biggest challenges when working with AI-generated content is ensuring that the output stays consistent and relevant. To address this, we carefully crafted our prompts to provide clear instructions and constraints.

Here's how we set up our guardrails:

1. Word Limits: We explicitly instructed ChatGPT to keep the headline under 10 words and the subheadline under 15 words.
2. Relevance: The prompt emphasized the importance of accessibility, WCAG compliance, and related terms, ensuring the output stayed on-topic.
3. JSON Output: By asking for a structured JSON response, we eliminated unnecessary text or explanations from ChatGPT.

For example, if the visitor was from the "Retail" industry, ChatGPT might generate:

 

{
    "headline": "Empowering Retail with Accessibility Solutions",
    "subheadline": "Helping businesses meet WCAG standards for inclusive experiences."
}

 

By carefully engineering our prompts, we achieved consistent, professional, and actionable results without requiring post-processing. Also - to do project this quickly, we used the most simple OpenAI endpoint – chat/completions – which required us to send the prompt instructions each time. The more elegant way would be to utilize the Assistants endpoint, which gives more fine-tuning capabilities on the backend. But for now, this works well enough.

Step 4: Displaying Dynamic Content on the Landing Page

Once we had the generated content, it was time to dynamically update the landing page. Using JavaScript, we fetched the PHP API response and updated the page's <h1> and <h2> elements with the personalized headline and subheadline.

Then, we did some minor massaging via JS to match our landing page framework. Ideally we wouldn't have to do this, but, we were in a hurry.

Here's the workflow:

1. On page load, the script checks for query parameters (company name and industry/vertical). If they're present, it skips Snitcher and sends the data directly to the PHP API. This is mostly for testing, but you never know.
2. If query parameters are absent, Snitcher identifies the visitor and passes the company and industry data to the JavaScript.
3. The data is sent to the PHP API, which calls OpenAI's API to generate the content.
4. The generated headline and subheadline are displayed on the page dynamically, ensuring a tailored experience for each visitor.

Fallback mechanisms were included to display default content if visitor data was unavailable or the API request failed. This ensured the page always provided a professional experience, even under edge cases.

Step 5: Front-End Integration for Dynamic Content

After building the backend to generate personalized headlines and subheadlines, the final step was to integrate this functionality into the front-end for a seamless user experience. The goal was to dynamically update the landing page content in real-time, ensuring visitors immediately saw personalized messaging. This is very simple via Javascript.

How We Integrated the Front-End

Fetching Data:

• The visitor's company and industry information was retrieved using Snitcher's API.
• This data was sent to the PHP API, which returned a JSON response containing the generated headline and subheadline.

Dynamic Content Updates:

• Once the response was received, the <h1> and <h2> elements on the page were updated with the personalized content.
• For added emphasis, we highlighted the certain words of the headline using <mark> tags to draw attention to key terms.

Fallbacks for Reliability:

• If visitor identification failed or the API request returned an error, we displayed default, non-personalized content to ensure a professional and consistent experience.

How It Feels in Action

Here's an example of the final result:

A visitor from the "Retail" industry might see:

• Headline: "Empowering Retail with Accessibility Solutions"
• Subheadline: "Helping brands meet WCAG standards for an inclusive experience."

This transformation happens in real-time as the page loads, creating a tailored experience that feels unique to each visitor.

What's Next

This system is just the beginning. In the future, we plan to:

• Personalize additional page elements like calls-to-action or images.
• Implement analytics to measure the effectiveness of personalized content.
• A/B test variations of headlines and subheadlines to maximize engagement and conversions.

Wrapping It Up

This project demonstrated the power of combining tools like Snitcher, OpenAI and ChatGPT to deliver personalized experiences with minimal effort. In under an hour, we created a system that:

• Identifies visitors in real-time using Snitcher's API.
• Uses OpenAI's capabilities to generate tailored headlines and subheadlines within strict guardrails.
• Dynamically updates the front-end to display personalized content seamlessly.
• Installed some safety and protections to prevent rogue abuse of the API call.

What does this mean for you and your company? It means that you can do this too. This should be a time for quick experimentation – trying things often and then determining if they are viable or should be abandoned. Never before has this type of workflow been possible. They key is to just get started.

If you're interested in using AI and real-time personalization to improve your website, let's talk! We'd love to help you build something extraordinary.